Unstructured information represents the largest, most current and fastest
growing source of knowledge available to businesses and governments worldwide.
The web is just the tip of the iceberg.
Consider the droves of corporate and technical documentation ranging from best
practices, research reports, medical abstracts, problem reports, customer
communications and contracts to emails and voice mails. Beyond these consider
the growing number of broadcasts containing audio, video and speech. In these
mounds of natural language, speech and video artifacts often lay nuggets of
knowledge critical for analyzing and solving problems, detecting threats,
realizing important trends and relationships, creating new opportunities or
preventing disasters.

— Shaving off just seconds per call to find the right technical documentation in
call-centers can save millions of dollars.

— Rapidly detecting emerging trends in problem-reports coming in from all over
the globe can avoid recalls and save companies and their customers millions if
not billions.

— Automating the analysis, segmentation and restructuring of educational content
to better serve changing skill sets or new learning objectives can save many
hours and can better enable just-in-time learning for critical tasks.

— Detecting otherwise unrealized drug interactions through analyzing the
linkages buried in millions of medical abstracts can help prevent disaster as
well as help discover new drugs or cures.

— Analyzing communications linked to terrorist networks in the form of
multi-lingual text, speech or video can help uncover plots threatening national
security before they happen.

These are just a few of the applications that can benefit from the exploitation
of unstructured information.

Applications like these, which rely on the rapid discovery of vital knowledge,
require the analysis of unstructured information. This is all the information
that has NOT been carefully encoded in enterprise databases but rather exists as
natural language text, speech or video. These applications rely on the rapid
assignment of semantics to huge volumes of unstructured content exactly so that
this content may be structured and exploited by traditional application
infrastructure (e.g., database management systems, knowledgebase systems,
information retrieval systems, etc.).

Unstructured information may be defined as the direct product of human
communication. Examples include natural language documents, email, speech,
images and video. It is information that was not specifically encoded for
machines to process but rather authored by humans for humans to understand. We
say it is "unstructured"
because it lacks explicit semantics ("structure") required for applications to
interpret the information as intended by the human author or required by the
end-user application.

Unstructured information may be contrasted with the information in classic
relational databases where the intended interpretation for every field data is
explicitly encoded in the database by column headings. Consider information
encoded in XML as another example. In an XML document some of the data is
wrapped by tags which provide explicit semantic information about how that data
should be interpreted. An XML document or a relational database may be
considered semi-structured in practice, because the content of some chunk of
data, a blob of text in a text field labeled "description" for example, may be
of interest to an application but remain without any explicit tagging-that is,
without any explicit semantics or structure.

For unstructured information to be processed by traditional applications, it
must be first analyzed to assign application- specific semantics to the
unstructured content. Another way to say this is that the unstructured
information must become "structured" where the added structure explicitly
provides the semantics required by target applications to interpret the data.

An example of assigning semantics includes wrapping regions of text in a text
document with appropriate XML tags that might identify the names of
organizations or products. Another example may extract elements of a document
and insert them in the appropriate fields of a relational database or use them
to create instances of concepts in a knowledgebase. Another example may analyze
a voice stream and tag it with the information explicitly identifying the
speaker.

A simple analysis on documents may, for example, scan each token in each
document of a collection to identify names of organizations. It may insert a tag
wrapping and identifying every found occurrence of an organization name and
output the XML that explicitly annotates each with the appropriate tag. An
application that manages a database of organizations may now use the structured
information produced by the document analysis to populate a relational database.

In general, we refer to the act of assigning semantics to a region of some
unstructured content (e.g., a document) as
"analysis". A software component or service that performs the
analysis is an "analytic".

The semantics are captured by an analytic as structure metadata elements. So
*analytics* implement operations that produce structured metadata elements
describing regions of the unstructured content which they analyze. The generated
metadata may be represented in many different ways including as XML tags.

We refer to systems that perform analysis on unstructured information as
"Unstructured Information Management (UIM) applications."

UIM applications tend to be highly decomposable; that is, they may be broken
down into many fine-grained *analytics*. Each of these performs some
constituent function in an overall analysis flow.

Analytics and Analysis Frameworks

Analytics may be reused in different flows to perform different aggregate
analyses. Even in our simple example above, a first, very common function, in
the overall process is to tokenize the document (identify each individual word).
This tokenization function may be reused as a first step in many different
analysis tasks for many different applications.

Many software frameworks have been developed in support of building and
integrating component analytics (e.g., Gate, Catalyst, Tipster, Mallet, Talent,
Open-NLP, etc.). However, no clear standard has emerged for enabling the
interoperability of analytics across modalities (text, audio, video, etc.),
frameworks and programming platforms in support of developing robust and
pluggable UIM applications.

The UIMA Java Framework is an implementation that arguably comes closest to
addressing the breadth of these requirements. It was originally developed as
part of the UIMA project at IBM Research (http://www.ibm.com/research/uima). It
provides a common, object- oriented and extensible means for representing
unstructured information and its metadata, a set of basic interface definitions
for implementing interoperable analytics and a Java run-time for supporting
analytic composition and deployment (of Java and C++ analytics).

The UIMA Java Framework was released in late 2004 as part of the UIMA Software
Developers Kit (SDK) on IBM AlphaWorks
(http://www.ibm.com/alphaworks/tech/uima). The SDK is freely available and
provides the tools and run-time necessary for creating, composing and deploying
component analytics. These may be implemented by the developer to analyze and
assign semantics to multi-modal data including, for example, combinations of
text, audio and video.

In early 2006 IBM contributed the UIMA Java Framework to the open-source
community through source forge (http://uima-framework.sourceforge.net/). The
open-source will soon be managed in a venue where IBM and non-IBM committers can
participate in its collaborative development. Since the framework's posting,
there have been over 8000 downloads of the framework by industry, government and
academia. It has been included in IBM Information Management products and used
in many solutions in areas ranging from life-sciences, to national security to
customer relationships management.

The Need for a Standard Specification

The UIMA Java Framework is an implementation tied to a particular programming
model and platform. It makes many system level commitments based on a variety of
design points. This implementation, however, suggests a more general
specification for interoperability that may allow for different framework
implementations and different levels of compliance supporting interoperability
for a broader range of application and programming requirements.

We propose to develop the UIMA Specification to explicitly define standard data
specifications, operation types and communication protocols to facilitate
interoperability of analytics at the data and services level.

This level of specification will serve a critical role in helping to facilitate
lighter-weight interoperability across a broader spectrum of platforms,
programming models, applications and tools for text and multi-modal analytics.

The intent is that the standard will allow different frameworks to emerge, while
also allowing applications built on different implementations to have a standard
means to share analysis data and services. It will lower the barrier for
component and application developers to interoperate at different levels
allowing a broader community to discover, reuse and compose a growing body of
text and multi-modal analytics.

Scope of the TC's work

The scope of the work of the TC is to generalize from the published UIMA Java
Framework implementation and produce a platform-independent specification in
support of the interoperability, discovery and composition of analytics across
modalities, domain models, frameworks and platforms.

Specifically, the TC is to consider an initial draft contributed by IBM in the
Research Report based on the UIMA project entitled "Towards an Interoperability
Standard for Text and Multi-Modal Analytics". This report should be used as a
straw man to scope, develop and rationalize a formal UIMA specification.

The TC will address three primary tasks

Elements of the Specification

Related Issues and Standards

Higher-Level Documentation

Elements of the Specification

The committee will be charged with evaluating, extending, modifying and refining
the proposed eight (8) elements of the UIMA specification. These elements are
dependent on other standards including UML, eMOF, eCore, XML Schema, XMI, OCL,
WSDL and SOAP.

Common Analysis Structure (CAS) Specification. Provides a simple and
extensible typed model for representing analysis data as a standard object model
that may be easily instantiated and manipulated in object-oriented programming
systems. This element of the specification is provided as a UML model. We
propose adopting the XML Metadata Interchange (XMI) specification
(http://www.omg.org/docs/formal/03-05-02.pdf) to provide a standard means for
representing analysis data as an XML document.

Type System Base Model. Provides a standard and extensible set of
domain-independent types generally useful for analyzing unstructured
information.

The Behavioral Metadata Specification. Provides a standard declarative means
for describing the capabilities of analysis operations in terms of what types of
CASs they can process, what elements in a CAS they can analyze, and what sorts
of effects they would have on CAS contents as a result. Behavioral metadata
would be used to assist in the discovery and composition of analytics based on
their described function. We propose appealing to the OCL standard
(http://www.omg.org/technology/documents/formal/ocl.htm)to represent behavioral
metadata.

Analytic Metadata Specification. Provides a standard declarative means for
describing identification, configuration and behavioral information about
analytics. This specification may be represented as a UML Model from which an
XML Schema may be generated. It refers to the Behavioral Metadata Specification
to represent an analytic's behavioral information

.

Aggregate Analytic Metadata Specification. Provides a standard declarative
means for an aggregate analytic to: a. refer to its constituent analytics, b.
identify a flow controller, which determines the order in which the constituent
analytics of the aggregate are invoked on a CAS and c. define mappings to
facilitate the composition of independently-developed analytics.

Abstract Interfaces. Abstractly describes the interfaces to the two
different types of components or services that developers may implement, namely,
Analytics and Flow Controllers. These abstract interfaces may be specified with
a UML model.

Service Descriptions and SOAP Bindings. Provide a standard means for
implementing Analytics and Flow Controllers as web services using SOAP. This
specification may be represented using WSDL (http://www.w3.org/TR/wsdl20/).

Related Issues, Requirements and Standards

In addition, the UIMA TC will be charged with providing recommendations
regarding how other requirements should or should NOT be addressed or related
to by the UIMA specification
including:

CAS representations for efficient stream operations

Representing and Recording Provenance Information

Privacy and Security Issues

General alignment with ontologies and related representational standards including OWL and RDF

Discovery-services in support of finding analytics based on identification and behavioral metadata

Analytic configuration management

High-Level Documentation

The UIMA TC should produce higher-level documentation to help motivate and
promote the UIMA specification as a standard that may include use-cases,
case-studies and high-level architectural descriptions but excludes detailed
formalizations.

Out of Scope

Finally, the UIMA TC will NOT address platform-dependent specifications
including the definition programming models or object-oriented APIs, the binding
of interfaces to any particular programming language, workflow engines or
languages, the implementation or integration of system middleware services to
address the scalability, componentization or life-cycle management of framework
implementations. The UIMA TC would NOT define any specific domain model (e.g.,
set of XML tags or types) for marking up unstructured information.

Deliverables

Initial Use Cases — 2Q 2007

The CAS Model — 3Q 2007

The CAS XMI Specification — 3Q 2007

The Type System Language — 3Q 2007

The Type System Base Model — 3Q 2007

Behavioral Metadata — 4Q 2007

Analytic Metadata — 4Q 2007

Aggregate Analytic Metadata — 4Q 2007

Abstract Interfaces — 4Q 2007

Service WSDL Descriptions — 4Q 2007

Recommendations regarding related requirements — 4Q 2007

Appendix: Soap Bindings — 4Q 2007

Appendix: Java Framework Compliance Notes — 4Q 2007

Appendix: Design Patterns — 4Q 2007

Anticipated audience

UIMA Java Framework developers

Text Analysis Vendors

Search and Knowledge Discovery Vendors

Document Management Vendors

Video and Speech Analysis Vendors

Machine Translation Vendors

Government Contractors

US and other Government agencies

R&D in Life-Sciences and BioInformatics

Universities performing research in text & multi-modal analytics

Publishing

Language

The TC will conduct its business in English. The TC may elect to form subcommittees that produce localized documentation of the TC's work in additional languages.